Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly
Journal of Financial Econometrics (2020, forthcoming) and University of Zurich, Department of Economics, Working Paper No. 290, Revised version
28 Pages Posted: 12 Jun 2018 Last revised: 14 Oct 2020
Date Written: December 1, 2018
This paper injects factor structure into the estimation of time-varying, large-dimensional covariance matrices of stock returns. Existing factor models struggle to model the covariance matrix of residuals in the presence of time-varying conditional heteroskedasticity in large universes. Conversely, rotation-equivariant estimators of large-dimensional time-varying covariance matrices forsake directional information embedded in market-wide risk factors. We introduce a new covariance matrix estimator that blends factor structure with time-varying conditional heteroskedasticity of residuals in large dimensions up to 1000 stocks. It displays superior all-around performance on historical data against a variety of state-of-the-art competitors, including static factor models, exogenous factor models, sparsity-based models, and structure-free dynamic models. This new estimator can be used to deliver more efficient portfolio selection and detection of anomalies in the cross-section of stock returns.
Keywords: Dynamic conditional correlations, factor models, multivariate GARCH, Markowitz portfolio selection, nonlinear shrinkage
JEL Classification: C13, C58, G11
Suggested Citation: Suggested Citation